融合感知与规划，实现拖挂卡车自动泊车。
Parking Assistance for Trailer-Truck Transport Vehicles Using Sensor Fusion and Motion Planning
- 用传感器融合+混合A*规划，处理拖挂车复杂运动
- 在仿真中成功实现拖挂车路径规划，避免急弯
- 适合研究自动驾驶泊车或车辆动力学的工程师
过去十年间，自动驾驶技术迅速发展，在提升运输效率、安全性和降低成本方面成效显著。尽管高速行驶和避障已取得进展，低速操作如泊车仍是自主系统的主要挑战，尤其对于具有铰接结构且受环境限制的拖挂卡车而言更为困难。本文提出一种集成感知、运动规划、控制系统与基础设施感知的自主卡车泊车框架。通过融合传感器数据、采用混合A*路径规划、非线性模型预测控制（NMPC）以及数据驱动的泊车系统，强调系统级协同对可靠、可扩展自主泊车解决方案的重要性。作为概念验证，我们基于开源A*路径规划仿真平台，引入了拖挂车运动学模型，在命令行仿真环境中实现了铰接车辆路径规划，识别出防折角（jackknife）仍需进一步改进。
原文摘要 · Abstract (English)
Autonomous driving technology has rapidly evolved over the past decade, offering significant improvements in transportation efficiency, safety, and cost reduction. While much of the progress has focused on highway driving and obstacle avoidance, low-speed maneuvers such as parking remain among the most difficult challenges for autonomous systems. This challenge is especially pronounced in trailer-truck transport vehicles due to their articulated motion and environmental constraints. This paper presents a proposed framework for autonomous truck parking that integrates perception, motion planning, control systems, and infrastructure awareness. By combining sensor fusion, Hybrid A* path planning, nonlinear model predictive control (NMPC), and data-driven parking systems, this work highlights the importance of system-level coordination for reliable and scalable autonomous parking solutions. As a proof-of-concept implementation, we adapted an open-source A* path planning simulation to incorporate a tractor-trailer kinematic model, demonstrating articulated vehicle path planning within a command-line simulation environment, with jackknife prevention identified as an area requiring further development.
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